Correlation factors influencing charging behavior of new-energy ride-hailing users based on SEM model
This study explores the complex interactions among charging behavior and various factors within the context of new-energy ride-hailing users, including personal attributes, vehicle characteris-tics, and user satisfaction. Utilizing Structural Equation Modeling (SEM) to meticulously scrutinize the influence of these quantitative factors and leveraging association rule mining techniques to dissect 405 samples combining categorical and quantitative data, the research identifies distinct patterns in decision-making related to users and charging behavior. The results indicate that with every additional 40 kilometers driven, the number of charging sessions increases by an average of 0.407. Moreover, user and vehicle attributes exhibit variances in charging decisions across diverse groups, with path coef-ficients of 0.336 and 0.159, respectively. The queue time threshold, when increased, elevates the ar-rival time threshold by an average of 0.231 units per additional unit, indicating a consistent acceptance of time thresholds across varied scenarios and emphasizing the potential for tailored services to cater to heterogeneous user needs. The accuracy of estimated range for new energy vehicles correlates posi-tively with user satisfaction regarding charging facilities, with a P-value of 0.273, underscoring the pivotal role of precise range estimation in driving the adoption of new energy vehicles. The complex re-actions to price and service suggest the potential value of flexible pricing mechanisms in the market, while the intricate balance between time and money in decision-making underscores the multifaceted and diverse nature of these behaviors. These findings unveil the intricate associations among different factors, providing a theoretical framework and empirical substantiation for future research in related do-mains.
万方数据
维普
